Data center total resource utilization efficiency (TRUE) system and method

ABSTRACT

Embodiments disclosed include methods and systems that adaptively, in real-time, evaluate data center performance, assess data center efficiency, data center sustainability, data center availability, compute performance, storage performance and provide data center customers with an overall data center performance rating, presented as a Total Resource Utilization Efficiency or TRUE score. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods. Other embodiments of the methods or systems include addition of newly defined metrics as categories or sub-categories to be used to calculate data center TRUE score.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. Application Ser. No.17/107,954, filed Dec. 1, 2020, which is a Continuation of U.S.application Ser. No. 15/663,782, filed Jul. 30, 2017, now U.S. Pat. No.10,852,805, issued. Dec. 1, 2020; U.S. application Ser. No. 17/107,954is also a Continuation of U.S. application Ser. No. 16/248,770, filedJan. 16, 2019, now U.S. Pat. No. 11,262,089, issued Mar. 1, 2022, whichis a Continuation of U.S. application Ser. No. 14/591,572, filed Jan. 7,2015, now abandoned, which claims priority to U.S. Provisional PatentApplication No. 61/925,531, filed Jan. 9, 2014; U.S. application Ser.No. 17/107,954 is a Continuation of U.S. application Ser. No.15/283,097, filed Sep. 30, 2016, now U.S. Pat. No. 10,216,606, issued.Feb. 26, 2019, each of which is incorporated by reference herein in itsentirety.

BACKGROUND OF THE INVENTION Field

The present invention generally relates to data centers, in particularto methods and systems to evaluate data center performance, assess datacenter efficiency, data center sustainability, data center availability,compute performance, storage performance and to provide data centercustomers with an overall data center performance rating.

Related Art

A data center is a facility used to house computer systems andassociated components. The computer systems, associated componentshoused in data centers and the environmental control cooling systemstherein, consume significant amounts of energy. With the typical moderndata center requiring several megawatts (MW) of power to support andcool the computer systems and associated components therein, resourceutilization efficiency has become critical to evaluating data centerperformance.

To support the power consumption of the computer systems, associatedcomponents housed in the data centers and environmental control coolingsystems, data centers consume a significant amount of water annually.Data center cooling system efficiency is critical to reduce the numberof liters of water used per kilowatt hour (kWh) of energy consumed bythe computer systems and associated components housed in the datacenter.

Data centers are key components of business continuity, with businessesheavily reliant on data center high availability to support missioncritical data driven applications and services. Data center outagesadversely impact business revenue. A single minute of a data centeroutage along with the time for recovering mission critical applicationsand services can mean thousands or millions of dollars of lost revenue.As such data center availability or uptime is another important factorin evaluating data center performance.

Prior art methods and systems have attempted to develop multi metricviews to provide a broader understanding of data center performance.These multi metric views often take into account a single aspect of datacenter performance, Power Usage Effectiveness (PUE), a measure of howefficiently a data center uses energy. However, there still remains aneed for a more nuanced and multi-dimensional metric that addresses thecritical aspects of data center performance. In order to establish amore complete view of data center performance, there exists arequirement to assess key aspects of data center performance such asdata center efficiency, data center availability and data centersustainability. There remains an additional need for a multi-dimensionalmetric that is easily scalable and that can accommodate additional newmetrics in the future, as they are defined. Embodiments disclosedaddress precisely such a need.

SUMMARY

One general aspect includes a method and system of one or more computersconfigured to perform particular analysis of data center performancemetrics and factors by virtue of having software, firmware, hardware, orcombinations of them installed on the system that in operation causes orcause the system to perform the actions. One or more computer programscan be configured to perform particular operations or actions by virtueof including instructions that, when executed by data processingapparatus, cause the apparatus to perform the actions. One generalaspect includes a computer automated system configured to adaptively, inreal-time, assess multiple data center performance metrics and factorsincluding but not limited to, data center efficiency, power usageeffectiveness (PUE), water usage effectiveness (WU), data centersustainability and environmental impact, greenhouse gas ((MG) intensity,carbon intensity, particulate matter intensity, water chemicalintensity, compute performance, storage performance, data centerlocation, climate zone, and data center availability.

Power usage effectiveness (PUE) is essentially a measure of howefficiently a data center uses energy; specifically, how much energy isused by the computing equipment in contrast to cooling and otheroverheads. PUE is the ratio of total amount of energy used by a computerdata center facility to the energy delivered to computing equipment(compute system, storage system, networking system, etc.) Total FacilityEnergy equals everything beyond the Information Technology (IT)infrastructure (compute/storage/networking) equipment power usage.

Water usage effectiveness (WUE) is essentially a measure of howefficiently a data center uses water; specifically, how much water isused annually to cool the data center. Water usage effectiveness (WUE)is calculated using the annual data center water consumption anddividing it by the total annual energy consumption of the computersystems and associated components therein. The result of the calculationrepresents data center water consumption efficiency in the form ofliters/kilowatt hour or L/kWh. Similar to Scope 1 and Scope 2 metricsfor emissions, WUE takes into consideration both remote water usages atthe power source generation and onsite water usage.

Environmental impact and sustainability is quantified using greenhousegas (GHQ) intensity, carbon intensity and particulate matter intensity.The intensity of each is a measurement of the number of units producedper megawatt hour (MWh).

Water chemical intensity is essentially a measure of how many units ofchemicals are used annually at a data center for water treatment and iscalculated using the number of units used for water treatment permegawatt hour (MWh)

Compute and storage performance is determined using well knownbenchmarks to calculate the maximum compute performance and storageperformance within the data center power and codling specifications.

Location and climate zones are factors in determining environmentalimpact, PETE and WUE efficiency

Data center availability is essentially a measurement of the totalannual downtime of a data center. This is a critical factor indetermining risk assessment and evaluating overall data centerperformance.

Based on the determined values of multiple data center performancemetrics and factors, the system will calculate a date center performancerating. This will be known as the data center Total Resource UtilizationEfficiency, or TRUE Score.

Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an embodiment of the Data Center InfrastructureManagement, or DCIM system.

FIG. 2 illustrates a block diagram depicting data collection and computedensity efficiency (CLUE) calculation in the DCIM system.

FIG. 3 depicts a logical view of the DCIM system according; to anembodiment.

FIG. 4 depicts the system and method implementing complete computingresource consumption estimation over each node of a network of connecteddata centers.

DETAILED DESCRIPTION

The following is a detailed description of embodiments of the inventiondepicted in the accompanying drawings. The embodiments are introduced insuch detail as to clearly communicate the invention. However, theembodiment(s) presented herein are merely illustrative, and are notintended to limit the anticipated variations of such embodiments; on thecontrary, they intention is to cover all modifications, equivalents, andalternatives falling within the spirit and scope of the appended claims.The detailed descriptions below are designed to make such embodimentsObvious to those of ordinary skill in the art.

As stated above, the traditional way of monitoring data centerinfrastructure, collecting data from infrastructure systems, andmanaging the systems to allow maximizing the operational efficiency isnow struggling to cope with new challenges brought by the growingcomplexity of data centers. Embodiments disclosed include systems andmethods that address these challenges effectively and efficiently.

According to an embodiment a computer automated system is configured toadaptively, in real-time, determine based on an operating condition of acompute system, a storage system, and a power management system housedin a facility, a maximum power unit efficiency or power usageeffectiveness (PUE). The compute system and the storage system arecomprised in a networked computer system comprising a plurality ofprocessing units coupled to a corresponding plurality of memory elementshaving encoded thereon instructions implemented by the plurality ofprocessing units. Based on the determined maximum power unit efficiencyor power usage effectiveness (PUE), the computer automated system isconfigured to calibrate at least one of the compute system, the storagesystem, the power management system and the operating condition of thefacility.

According to an embodiment of the computer automated system, determiningthe optimum power unit efficiency or power usage effectiveness (PUE)comprises further determining a total power requirement of the facility.Preferably, the computer automated system is further configured todetermine a total power requirement of an input-output system alsocomprised in the facility, the compute system and the storage systemcomprised in the facility. Additionally, according to an embodiment, thecomputer automated system is further configured to determine a totalarea occupied by the facility. Preferably, the computer automated systemcan aggregate the total power requirement of the input-output system,the compute system and the storage system and is configured to determineand display a result. The result comprises the total power requirementdivided by the aggregated total power requirement of the input-outputsystem, the compute system and the storage system and further multipliedby the reciprocal of the determined total area occupied by the facility.

Power usage effectiveness (PUE) is essentially a measure of howefficiently a computer data center uses energy; specifically, how muchenergy is used by the computing equipment in contrast to cooling andother overheads. PUE is the ratio of total amount of energy used by acomputer data center facility to the energy delivered to computingequipment (compute system, storage system, networking system, etc.)Total Facility Energy equals everything beyond the InformationTechnology (IT) infrastructure (compute/storage/networking) equipmentpower usage.

So, if the total power utilization for a data center is 2 MW and the ITequipment power utilization is 1 MW then the PUE is 2.0.

PUE=Total Facility Energy/IT Equipment Energy

Preferred embodiments include systems and methods for Total ResourceUtilization Efficiency (TRUE) estimation, and optimization. The PUE isone of the critical components. However, the PUE is determined by andcontributes towards determining other critical components in TotalResource Utilization Efficiency estimation and optimization. Accordingto an embodiment, other variables include (but are not limited to)operating expense of a data center, kilowatts (1 KW) per rack basedpower consumption monitoring and optimization, Server utilizationestimation and optimization, Network utilization estimation andoptimization, Storage utilization estimation and optimization, and Wattsconsumed per unit area, for example, per square Toot. Preferredembodiments further include Water Usage Effectiveness (WUE), Carbon UnitEffectiveness (CUE) and Total Carbon footprint of the data center.Alternatives to the above order and variables are possible, and evenprobable, as would be apparent to a person having ordinary skill in theart.

According to an embodiment, the computer automated system is configuredto determine a real-time operating condition of the compute system.Based on the determined real-time operating condition of the computersystem, the computer automated systems are further configured tocalibrate the compute system to operate at a load that allows maximumenergy efficiency. Preferably, the calibrating is based on a pre-defineddata management policy comprising determining data to retain and aretention period required for the retained data, and accordinglydetermining data to migrate to another networked computer system in thefacility or to another networked compute system in another networkedfacility.

FIG. 1 illustrates an embodiment of the Data Center InfrastructureManagement (DCIM) System. The illustrated embodiment includes aprocessing 100 coupled to a memory element 104, and having instructionsencoded thereon configured to: over a network 114, collect computesystems data, power systems data, and facility systems data from datacenters 116A, 116B, and 1160. The disclosed embodiment is configured totrigger an action based on a diagnosed or predicted condition of thecollected compute systems, power systems and facility systems. Accordingto an embodiment, the configuration enables control of the computesystems, power systems and facility systems in each of the illustrateddata centers via a corresponding centralized compute module 108, powermodule 110, and facilities module 112. Preferably, the control via thecompute, power, and facilities module comprises calibrating the compute,power, and facility systems based on an estimated compute requirement,and an associated power, cooling, and network data resource requirement.According to an embodiment, the estimated compute requirement comprisesestimating compute density per real-time power wattage, and storagedensity per real-time power wattage.

FIG. 2 illustrates a block diagram depicting data collection and computedensity efficiency (CDE) calculation the DCIM system. With the computedata, power data and facilities data input into the DCIM system, thesystem estimates compute density per real-time power wattage, andstorage density per real-time power wattage, and outputs the result todashboards, networked User Interfaces and Export. According to anembodiment the Export could be presented in virtual reality anddisplayed on a smart phone, or other portable computing device.

According to an embodiment, the system is further configured to, basedon the collected data center compute systems, power systems, andfacility systems data, estimate a future compute systems condition, afuture power systems condition, and a future facility systems conditionas one type of Export showed in FIG. 2 .

FIG. 3 illustrates via a flow diagram, the method of accommodatingdifferent metrics to adjust considering Performance Indicator tooptimize the operation of the system. Step 302 includes choosing theconsidering Performance indicator. In step 304, the related data iscollected from the data center or predicted by the processor. In step306, a decision is made based on the data aggregated in the memory fromimplemented machine learning to decide whether an adjustment orcalibration is needed. Step 308 is implemented wherein an adjustment ismade to the system. If the in the step 306 the metric is optimal, thenthe system proceeds to find or input manually another PerformanceIndicator and repeat the step 304 until the data center operation isoptimized.

FIG. 4 depicts the system and method implementing complete computingresource consumption estimation over each node of a network of connecteddata centers. Preferred embodiments implement Total Resource UtilizationEfficiency (TRUE) optimizing not just compute resource consumption, buttotal efficiency of all components in a facility. According to theembodiment, the system is configured to: determine, for each computesystem resource 400, a cost per predetermined time unit to deploy andoperate the compute system resource, and to apply a cost conversionfactor to each cost per predetermined time unit. Additionally, for eachcompute resource, the system generates an average number of resourceunits by averaging the number of resource units 402 over a plurality ofnetwork infrastructure nodes. And fix an application executing on atleast one of the network infrastructure nodes 404, the system generatesa number of resource units used in a predetermined time period. Thus,the system can generate total resource consumption 406 by adding thenumber of units consumed by the application in the predetermined timeperiod for each compute resource.

According to an embodiment, the computer automated system is configuredto determine an air intake requirement and a humidity requirement, andbased on pre-defined criteria, to raise or lower the air intake andhumidity according to the determined requirement. Preferably thedetermination is made based on the determined real-time operatingcondition of the compute system, the storage system, and the powermanagement system housed in the facility.

Embodiments disclosed include systems and methods to determine thereal-time operating condition of the facility, and based on thedetermined real-time operating condition of the facility, the computerautomated system is further configured to minimize the powerrequirement, wherein the minimization comprises automatic activation anddeactivation of lighting equipment.

According to an embodiment, and based on the determined real-timeoperating condition the computer automated system is further configuredto determine a heat generated via a plurality of temperature sensors orsensor elements, but preferably based on an operating load, andaccordingly control a re-configurable cooling equipment in the facility.Preferred embodiments include machine learning and artificialintelligence capability enabling the computer automated system topredictively determine a heat generated based on current and anticipatedloads, and accordingly anticipate state changes to optimize resourceutilization. Additionally, the reconfigurable cooling equipment isconfigured to automatically scale up or down according to the operatingload.

Embodiments disclosed include networked virtual reality interfacesenabling and facilitating remote inspection, calibration and control ofthe compute system, the storage system, the power system, and thefacility.

According to an embodiment, the computer automated system is furtherconfigured to, based on the determined real-time operating condition ofthe compute system, the storage system, and the power management systemhoused in the facility, and accordingly based on a determined powerrequirement, deploy a modular uninterrupted power source supply,configured to optimize efficiency at partial or complete loads.

According to an embodiment, a computer implemented method comprisingadaptively, in real-time, determining based on an operating condition ofa compute system, a storage system, and a power management system housedin a facility, a maximum power Unit efficiency Or power usageeffectiveness (PUE). Based on the determined maximum power unitefficiency or power usage effectiveness (PUE), the method comprisescalibrating at least one of the compute system, the storage system, thepower management system and the operating condition of the facility.

According to an embodiment, the computer implemented method furthercomprises determining an optimum power unit efficiency or power usageeffectiveness (PUE). This further comprises determining a total powerrequirement of the facility. Preferably, determining the total powerrequirement of the facility further comprises determining a total powerrequirement of the input-output system, the compute system and thestorage system comprised in the facility, and determining a total areaoccupied by the facility. Preferably the method comprises aggregatingthe total power requirement of the input-output system, the computesystem and the storage system and determining and displaying a result.The result comprises comprising the total power requirement divided bythe aggregated total power-requirement of the input-output system, thecompute system and the storage system and further multiplied by thereciprocal of the determined total area occupied by the facility.

According to an embodiment, and based on the determined real-timeoperating condition of the compute system, the method comprisescalibrating the compute system to operate at a load that allows maximumenergy efficiency; and wherein the calibrating is based on a pre-defineddata management policy comprising determining data to retain and aretention period required for the retained data, and accordinglydetermining data to migrate to another networked compute system withinthe facility or to another networked compute system in another networkedfacility.

According to an embodiment, and based on the determined real-timeoperating condition of the compute system, the storage system, and thepower management system housed in the facility, the computer implementedmethod further comprises determining an air intake requirement and ahumidity requirement, and based on a pre-defined criteria, computerautomated system is configured to trigger the raising or lowering of theair intake and humidity according to the determined requirement.

Embodiments disclosed include methods for automatic activation anddeactivation of lighting: equipment, according to a determined real-timeoperating condition.

According to an embodiment, and based on the determined real-timeoperating condition the computer automated system is further configuredto determine a heat generated via a plurality of temperature sensors orsensor elements, but preferably based on an operating load, andaccordingly control a re-configurable cooling equipment in the facility.Preferred embodiments include machine learning and artificialintelligence capability enabling the computer automated system topredictively determine a heat generated based on current and anticipatedloads, and accordingly anticipate state changes to optimize resourceutilization. Additionally, the reconfigurable cooling equipment isconfigured to automatically scale up or down according to the operatingload.

Embodiments disclosed include networked virtual reality interfacesenabling and facilitating remote inspection, calibration and control ofthe compute system, the storage system, the power system, and thefacility.

According to an embodiment, and based on the determined real-timeoperating condition of the compute system, the storage system, and thepower management system housed in the facility, the computer automatedsystem is further configured to based on a determined power requirement,deploy a modular uninterrupted power source supply, configured tooperate at high efficiency modes at partial or complete loads.

According to an ideal embodiment, the system and method includesestimating the Total Resource Utilization Efficiency (TRUE) (registeredtrademark) of the data center facility, and based on pre-configuredcriteria, rating the data center facility according to the calculatedTotal Resource Utilization Efficiency. The TRUE rating system referencesseveral categories in calculating a TRUE score. The categories compriseEfficiency, Availability, Environmental Impact, Compute, and Storage.Efficiency in turn comprises Power Unit Efficiency (PUE) also known asEnergy Efficiency, and Water Unit Efficiency (WUE) Availability in turn,comprises UPTIME percentage (%) based on total annual incident time. Forexample, 99.999% uptime is equal to 5 minutes 15.6 seconds of totalannual down time. The Environmental Impact is calculated by aggregatingseveral environment impact variables comprising GHG intensity, CarbonIntensity, Particle Matter Intensity, SO₂/NO_(x) intensity, and Watertreat chemicals.

Environmental impact and sustainability is quantified using greenhousegas (GHG) intensity per MWHh, carbon intensity per MWh and particulatematter intensity per MWh. The intensity of each emission type is ameasurement of the number of units (typically metric tons) produced permegawatt hour (MWh). GHG, SO₂, NO_(x) and other emissions may berepresented as carbon dioxide equivalent (CO2e).

The system or method may also be used to calculate carbon offsets orcarbon credits. Renewable energy sources for example can be selected asthe power source or as a percentage of the required power source withthis being used to calculate the total carbon offsets or carbon creditsthat can be quantified as an annual number.

Water treatment chemical intensity is essentially a measure of how manyunits of different types of chemicals are used annually at a data centerfor water treatment and is calculated using the number of units used forwater treatment per megawatt hour (MWh)

According to an embodiment, the intensity of the environmental impactvariables is calculated in metric tons per megawatt hour of energyconsumed. For example:

Green House Gas (GHG) intensity per MWh=metric tons/MWh

Carbon intensity per MWh=metric tons/MWh

Particle Matter intensity per MWh=metric tons/MWh

SO₂/NO_(x) intensity per MWh=metric tons/MWh

Water treatment chemical intensity is calculated on an annual basis andis represented as liters per kWh or L/kWh.

According to an example embodiment, if the carbon intensity is 0.5metric tons per. MWh, you can use that to calculate the total amount ofcarbon produced annually. Thus 100,000 MWh*0.5 metric tons=50,000 metrictons of carbon produced annually operating the data center.

Power usage effectiveness (PUE) is essentially a measure of howefficiently a data center uses energy; specifically, how much energy isused by the computing equipment in contrast to cooling and otheroverheads. PUE is the ratio of total amount of energy used by a computerdata center facility to the energy delivered to computing equipment(compute system, storage system, networking, system, etc.) TotalFacility Energy equals everything beyond the Information Technology (IT)infrastructure (compute/storage/networking) equipment power usage.

Water usage effectiveness (WUE) is essentially a measure of howefficiently a data center uses water; specifically, how much water isused annually to cool the data center. Water usage effectiveness (WUE)is calculated using the number of liters of water consumed per kilowatthour (kWh)

Compute and storage performance is determined using well knownbenchmarks to calculate the maximum compute performance and storageperformance within the data center power and cooling specifications.

Location and climate zones are factors in determining environmentalimpact, PUE and WUE efficiency.

According to an embodiment, Compute implies compute performance whichpreferably is calculated estimated by compute performance index per wattor kilowatt (KW) or megawatt (MW), etc. Preferably, compute performanceindex is based on a benchmark performance index multiplied by an averageutilization per watts. The result is multiplied by 100.

Compute performance index per watt=(benchmark performance index*averageutilization/wattS)*100. This result represents a compute performance perwatt value. Thus, for example if we have benchmark performance index of150 and an average utilization of 0.70 and a total of 300 watts, ourcompute performance index is (150*0.70/300)*100=35. Any benchmark may beused as long as the same benchmark performance system or method is usedfor all compute performance index calculations.

According to an embodiment, in calculating and rating TRUE, Storageimplies a storage performance index per watt or per kilowatt (KW) or permegawatt (MW), etc. Preferably, storage performance index is based on abenchmark performance index multiplied by an average utilization anddivided by the total watts with the result multiplied by 100.

Storage performance index per watt=(benchmark performance index*averageutilization/watts)*100. This result gives a storage performance per wattvalue. If the benchmark performance index is 150 with an averageutilization of 0.70 and a total of 150 watts, our storage performanceindex is (1.50*0.70/150)*100=70. Any bend mark may be used as long asthe same benchmark performance system or method is used for all storageperformance index calculations.

Data center availability is essentially a measurement of the totalannual downtime of a data center. This is a critical factor indetermining risk assessment and evaluating overall data centerperformance. The availability index is an important factor to determinethe data center operation efficiency since two data centers both builtto the same Tier level can have very different operation efficiencyratings, with one data center never having outages and the other datacenter having multiple incidents causing data center outages.

Based on the determined values of multiple data center performancemetrics, factors and indexes, the system or method calculates a datecenter performance rating. This is known as the data center TRUE score.

Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

According to an embodiment each of the TRUE categories andsub-categories is rated from 0 to 5. With 5 as the top rating and 0 asthe lowest rating. Each category is assigned a rating from 0 to 5 withthe rating based on the average of all the sub-categories within. Witheach category rated the average of the aggregated ratings of eachcategory is calculated to derive the TRUE score for the data center.Preferably, each category is rated based on the average of thesub-category ratings.

According to an additional embodiment, each category and sub-category isassigned a weighting based on pre-defined criteria. The following aresome example permutations. Variations and modifications are possible,even desirable, as would be apparent to a person having ordinary skillin the art.

Weighting

Sub-category ratings=rating/max rating=rating x.xx=rating percentage0-100%.

Sub-category weighting=entire parent category accounts for 100% ofweighting value, with each sub-category assigned a percentage of thetotal and all sub-category weighting percentages totaling 10%.

Category weighting=All categories combined account for 100% of weightingvalue, with each category assigned percentage of the total.

Calculating Score

Category rating base on SC ratings within that Category

Category ratings=rating/max rating=rating x.xx=rating percentage 0-100%

Category weighting=entire parent category accounts for 100% of weightingvalue, with each sub-category assigned a percentage of the total and allsub-category weights totaling 100%

All Category ratings are used to calculate TRUE score and expressed as apercentage with the perfect score being 100%

It is understood that newly defined data center metrics, indexes, can beadded to the methods and systems as a category or sub-category used tocalculate a data center TRUE score. The methods and systems areunderstood to be flexible and expandable, in order to accommodate futuredata center efficiency, availability, sustainability or other yetunknown key metrics.

Embodiments disclosed enable drastic reduction in power consumptionthrough smart management of cooling power, and leveraging ofenvironmental conditions to optimize cooling power consumption. Systemsand methods disclosed enable huge savings in data center powerconsumption. Predictive analytics enable real-time computing powerconsumption estimation and thereby optimization of computing and coolingpower consumption.

Embodiments disclosed include systems and methods that leveragemulti-metric views that provide real-time actionable intelligence ondata center performance and cooling performance. These multi-metricviews attempt to take into account aspects of performance by bringingtogether the Power Usage Effectives (PU) ratio, IT Thermal Conformanceand IT Thermal Resilience thereby enabling real-time optimizationthrough correlation of computing, infrastructure and coolingperformance. Embodiments disclosed further enable nuanced andmulti-dimensional metric that addresses the most critical aspects of adata center's cooling performance. To establish a more complete view offacility cooling, the requirement to calculate cooling effectiveness andthe data center's future thermal state is also critical. Embodimentsdisclosed enable easily scalable multi-dimensional metrics that canaccommodate additional new metrics in the future, as they are defined.

Since various possible embodiments might be made of the above invention,and since various changes might be made in the embodiments above setforth, it is to be understood that all matter herein described or shownin the accompanying drawings is to be interpreted as illustrative andnot to be considered in a limiting sense. Thus, it will be understood bythose skilled in the art of infrastructure management, and morespecifically automated infrastructure management especially pertainingto data centers, that although the preferred and alternate embodimentshave been shown and described in accordance with the Patent Statutes,the invention is not limited thereto or thereby.

The figures illustrate the architecture, functionality, and operation ofpossible implementations of systems, methods and computer programproducts according to various embodiments of the present invention. Itshould also be noted that, in some alternative implementations, thefunctions noted/illustrated may occur out of the order noted in thefigures. For example, two blocks shown in succession may, in fact, beexecuted substantially concurrently, or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved.

The terminology used herein is for the purpose of describing embodimentsonly and is not intended to be limiting of the invention. As usedherein, the singular forms “a”, “an” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

In general, the routines executed to implement the embodiments of theinvention, may be part of an operating system or a specific application,component, program, module, object, or sequence of instructions. Thecomputer program of the present invention typically is comprised of amultitude of instructions that will be translated by the native computerinto a machine-accessible format and hence executable instructions.Also, programs are comprised of variables and data structures thateither reside locally to the program or are found in memory or onstorage devices. In addition, various programs described hereinafter maybe identified based upon the application for which they are implementedin a specific embodiment of the invention. However, it should beappreciated that any particular program nomenclature that follows isused merely for convenience, and thus the invention should not belimited to use solely in any specific application identified and/orimplied by such nomenclature.

The present invention and some of its advantages have been described indetail for some embodiments. It should be understood that although thesystem and process is described with reference to automated powermanagement and optimization in data centers, the system and process ishighly reconfigurable, and may be used in other contexts as well. Itshould also be understood that various changes, substitutions andalterations can be made herein without departing from the spirit andscope of the invention as defined by the appended claims. An embodimentof the invention may achieve multiple objectives, but not everyembodiment falling within the scope of the attached claims will achieveevery objective. Moreover, the scope of the present application is notintended to be limited to the particular embodiments of the process,machine, manufacture, composition of matter, means, methods and stepsdescribed in the specification. A person having ordinary skill in theart will readily appreciate from the disclosure of the present inventionthat processes, machines, manufacture, compositions of matter, means,methods, or steps, presently existing or later to be developed areequivalent to, and fall within the scope of, what is claimed.Accordingly, the appended claims are intended to include within theirscope such processes, machines, manufacture compositions of matter,means, methods, or steps.

I claim:
 1. A computer automated system configured to: adaptively, inreal-time, determine a Total Resource Utilization Efficiency (TRUE) of adata center facility; based at least in part on the determined TotalResource Utilization Efficiency (TRUE), calibrate at least one of apower unit, a water unit, a computer system, a storage system, a powermanagement system and an operating condition of the data centerfacility; determine an environmental impact based at least in part on aplurality of environmental impact variables comprising at least one of agreenhouse gas (GHG) intensity, a Particle Matter Intensity, and anSO2/NOX intensity; and calibrate, based at least in part on thedetermined Total Resource Utilization Efficiency (TRUE), the computersystem to operate at a load that allows maximum energy efficiency,wherein the calibrating is based at least in part on a pre-defined datamanagement policy comprising determining data to retain and determininga retention period required for the retained data, and determining datato migrate to another computer system.
 2. The computer automated systemof claim 1, wherein the plurality of environmental variables furthercomprises a carbon intensity, and wherein determining the Total ResourceUtilization Efficiency (TRUE) further comprises: determining theoperational availability of the data center facility over a time period;and determining the environmental impact during the time period.
 3. Thecomputer automated system of claim 1, wherein determining the TotalResource Utilization Efficiency further comprises: optimizing the powerusage efficiency or power usage effectiveness (PUE) which comprises:determining a total power requirement of the facility; determining atotal power requirement of the input-output system, the computer systemand the storage system comprised in the facility; determining a totalarea occupied by the facility; and aggregating the total powerrequirement of the input-output system, the computer system and thestorage system.
 4. The computer automated system of claim 3, whereinoptimizing the power usage efficiency or power usage effectiveness (PUE)further comprises: determining and displaying a result comprising thetotal facility power requirement divided by the aggregated total powerrequirement of the input output system, the computer system and thestorage system and further multiplied by the reciprocal of thedetermined total area occupied by the facility.
 5. The computerautomated system of claim 1, wherein: in determining the environmentalimpact the computer automated system is further configured to: aggregatethe plurality of environmental impact variables comprising greenhousegas (GHG) intensity, a Carbon Intensity, Particle Matter Intensity,SO2/NOX intensity, based at least in part on a number of units producedper megawatt hour (MWh).
 6. The computer automated system of claim 5,wherein: in determining the environmental impact the computer automatedsystem is further configured to: determine a water treatment chemicalintensity, based at least in part on the number of chemicals usedannually at the data center for water treatment, calculated using anumber of liters of chemicals used for water treatment per kilowatt hour(kWh) or L/kWh.
 7. The computer automated system of claim 1, wherein incalibrating the water unit, the computer automated system is configuredto: determine a water usage effectiveness (WUE) which further comprisesdetermining a quantum of water consumed per kilowatt hour (kWh) over atime period.
 8. The computer automated system of claim 1, wherein incalibrating the computer system, the computer automated system isfurther configured to optimize compute performance index which comprisesa benchmark performance index multiplied by an average utilization perwatt factor.
 9. The computer automated system of claim 1, wherein incalibrating the storage system the computer automated system is furtherconfigured to: optimize storage performance index which comprises abenchmark performance index multiplied by an average utilization perwatt factor.
 10. The computer automated system of claim 1, wherein indetermining the data center operational availability, the computerautomated system is further configured to measure and quantify the totalannual uptime of the data center.
 11. The computer automated system ofclaim 1, wherein: based at least in part on the determined real-timeoperating condition of the computer system, the storage system, and thepower management system housed in the facility, the computer automatedsystem is further configured to determine an air intake requirement anda humidity requirement, and based at least in part on pre-definedcriteria, raise or lower the air intake and humidity according to thedetermined requirement.
 12. The computer automated system of claim 1,wherein based at least in part on the determined real-time operatingcondition, the system is further configured to: minimize the powerrequirement, which minimization comprises automatic activation anddeactivation of lighting equipment.
 13. The computer automated system ofclaim 1, wherein based at least in part on the determined real timeoperating condition the system is further configured to: predictivelydetermine a heat generated based at least in part on an operating load,and accordingly control a re-configurable cooling equipment in thefacility; and wherein the reconfigurable cooling equipment is configuredto automatically scale up or down according to the operating load. 14.The computer automated system of claim 1, wherein the system is furtherconfigured to: in a virtual reality interface, facilitate inspection,calibration and control of the power unit, the water unit, the computersystem, the storage system, and the facility.
 15. The computer automatedsystem of claim 1, wherein: based at least in part on the determinedreal-time operating condition of the computer system, the storagesystem, and the power management system housed in the facility, thecomputer automated system is further configured to, based at least inpart on a determined power requirement, deploy a modular uninterruptedpower source supply, configured to optimize efficiency at partial orcomplete loads.
 16. The computer automated system of claim 3, whereinthe power usage effectiveness (PUE) comprises a ratio of a total amountof energy used by the data center facility to energy delivered to thecomputer system.
 17. The computer automated system of claim 13, whereinthe operating load is a current or an anticipated operating load. 18.The computer automated system of claim 1, further configured to generatea score for the data center facility based at least in part onpre-configured criteria and the determined Total Resource UtilizationEfficiency (TRUE).
 19. The computer automated system of claim 18,wherein the score is further based at least in part on a power usageefficiency or power usage effectiveness (PUE) of the data centerfacility.